ArticleMagnetic resonance in medicine2021
Highly accelerated free-breathing real-time phase contrast cardiovascular MRI via complex-difference deep learning.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- FlowVN Trained on a Single Dataset Enables Rapid Reconstruction of Highly Accelerated 4D Flow MRI Across Multiple Sites.Magnetic resonance in medicine · 2026Article
- Feasibility of Golden Angle Spiral Real-Time Phase Contrast MRI at 0.55T: A Single-Center Prospective Study.Bioengineering (Basel, Switzerland) · 2026Article
- Evaluation of Real-Time Cardiovascular Flow MRI Using Compressed Sensing in a Phantom and in Patients With Valvular Disease or Arrhythmia.Journal of magnetic resonance imaging : JMRI · 2025Article
- Aortopathy associated with bicuspid aortic valve: advances in clinical and hemodynamics research.Frontiers in physiology · 2025Review
- GRASP reconstruction amplified with view-sharing and KWIC filtering reduces underestimation of peak velocity in highly-accelerated real-time phase-contrast MRI: A preliminary evaluation in pediatric patients with congenital heart disease.Magnetic resonance in medicine · 2024Article
- Highly accelerated free-breathing real-time 2D flow imaging using compressed sensing and shared velocity encoding.European radiology · 2024Article
- Present and Future Innovations in AI and Cardiac MRI.Radiology · 2024Review
- The future of CMR: All-in-one vs. real-time CMR (Part 2).Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Review
- Deep Learning-Based Reconstruction for Cardiac MRI: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.IEEE transactions on bio-medical engineering · 2022Article
- Advances in machine learning applications for cardiovascular 4D flow MRI.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo develop and evaluate a real-time phase contrast (PC) MRI protocol via complex-difference deep learning (DL) framework.
methodsDL used two 3D U-nets to separately filter aliasing artifact from radial real-time velocity-compensated and complex-difference images. U-nets were trained with synthetic real-time PC generated from electrocardiograph (ECG) -gated, breath-hold, segmented PC (ECG-gated segmented PC) acquired at the ascending aorta of 510 patients. In 21 patients, free-breathing, ungated real-time (acceleration rate = 28.8) and ECG-gated segmented (acceleration rate = 2) PC were prospectively acquired at the ascending aorta. Hemodynamic parameters (cardiac output [CO], stroke volume [SV], and mean velocity at peak systole [peak mean velocity]) were measured for ECG-gated segmented and DL-filtered synthetic real-time PC and compared using Bland-Altman and linear regression analyses. Additionally, hemodynamic parameters were quantified from DL-filtered, compressed-sensing (CS) -reconstructed, and gridding reconstructed prospective real-time PC and compared to ECG-gated segmented PC.
resultsSynthetic real-time PC with DL showed strong correlation (R > 0.98) and good agreement with ECG-gated segmented PC for quantified hemodynamic parameters (mean-difference: CO = -0.3 L/min, SV = -4.3 mL, peak mean velocity = -2.3 cm/s). On average, DL required 0.39 s/frame to filter prospective real-time PC, which was 4.6-fold faster than CS. Compared to CS, DL showed superior correlation, tighter limits of agreement (LOAs), better bias for peak mean velocity, and worse bias for CO and SV. Compared to gridding, DL showed similar correlation, tighter LOAs for CO and SV, similar bias for CO, and worse bias for SV and peak mean velocity.
conclusionThe complex-difference DL framework accelerated real-time PC-MRI by nearly 28-fold, enabling rapid free-running real-time assessment of flow hemodynamics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.